3,194 research outputs found
Co-operative or coyote? Producers' choice between intermediary purchasers and Fairtrade and organic cooperatives in Chiapas
This study of organic and Fairtrade co-operatives in Mexico aims to find out why many coffee producers prefer not to join the certified co-operatives, despite their higher price offer. A study of costs of production of organic coffee concludes that it implies more work, but not necessarily higher yields. A main conclusion of the investigation is that the compulsory organic production methods deters many producers from entering the co-operatives, and that it is more attractive for producers with more free family labour, and less attractive for producers with very little coffee land. However, the study also shows that it is not only economic factors that influence the decisions of the producers on where to sell their coffee.
Previous studies have shown that Fairtrade and organic certification can bring higher incomes and more security into the lives of marginalized farmers (Bray et al. 2002, Martinez-Torres 2006, Jaffeee 2007) hence it is important to understand more about how these systems can achieve their aims. This study shows that although the smallest farmers are less likely to become a part of these systems, the farmers who do are also very poor and vulnerable. Also, co-operatives need to be economically viable organisations and the organic requirements ensure a market with a higher price for the product, while at the same time keeping the organization at a manageable size. It is therefore recommended to keep the organic production requirements as a criteria for producers entering the co-operatives
Featureless visual processing for SLAM in changing outdoor environments
Vision-based SLAM is mostly a solved problem providing clear, sharp images can be obtained. However, in outdoor environments a number of factors such as rough terrain, high speeds and hardware limitations can result in these conditions not being met. High speed transit on rough terrain can lead to image blur and under/over exposure, problems that cannot easily be dealt with using low cost hardware. Furthermore, recently there has been a growth in interest in lifelong autonomy for robots, which brings with it the challenge in outdoor environments of dealing with a moving sun and lack of constant artificial lighting. In this paper, we present a lightweight approach to visual localization and visual odometry that addresses the challenges posed by perceptual change and low cost cameras. The approach combines low resolution imagery with the SLAM algorithm, RatSLAM. We test the system using a cheap consumer camera mounted on a small vehicle in a mixed urban and vegetated environment, at times ranging from dawn to dusk and in conditions ranging from sunny weather to rain. We first show that the system is able to provide reliable mapping and recall over the course of the day and incrementally incorporate new visual scenes from different times into an existing map. We then restrict the system to only learning visual scenes at one time of day, and show that the system is still able to localize and map at other times of day. The results demonstrate the viability of the approach in situations where image quality is poor and environmental or hardware factors preclude the use of visual features
Low-Carbon Technologies in the Post-Bali Period: Accelerating their Development and Deployment. CEPS ECP Report No. 4, 4 December 2007
This report analyses the very broad issue of technology development, demonstration and diffusion with a view to identifying the key elements of a complementary global technology track in the post-2012 framework. It identifies a number of immediate and concrete steps that can be taken to provide content and a structure for such a track. The report features three sections dealing with innovation and technology, investment in developing countries and investment and finance, followed by an analysis of the various initiatives being taken on technology both within and outside the United Nations Framework Convention on Climate Change (UNFCCC). A final section presents ideas for the way forward followed by brief concluding remarks
Rhythmic Representations: Learning Periodic Patterns for Scalable Place Recognition at a Sub-Linear Storage Cost
Robotic and animal mapping systems share many challenges and characteristics:
they must function in a wide variety of environmental conditions, enable the
robot or animal to navigate effectively to find food or shelter, and be
computationally tractable from both a speed and storage perspective. With
regards to map storage, the mammalian brain appears to take a diametrically
opposed approach to all current robotic mapping systems. Where robotic mapping
systems attempt to solve the data association problem to minimise
representational aliasing, neurons in the brain intentionally break data
association by encoding large (potentially unlimited) numbers of places with a
single neuron. In this paper, we propose a novel method based on supervised
learning techniques that seeks out regularly repeating visual patterns in the
environment with mutually complementary co-prime frequencies, and an encoding
scheme that enables storage requirements to grow sub-linearly with the size of
the environment being mapped. To improve robustness in challenging real-world
environments while maintaining storage growth sub-linearity, we incorporate
both multi-exemplar learning and data augmentation techniques. Using large
benchmark robotic mapping datasets, we demonstrate the combined system
achieving high-performance place recognition with sub-linear storage
requirements, and characterize the performance-storage growth trade-off curve.
The work serves as the first robotic mapping system with sub-linear storage
scaling properties, as well as the first large-scale demonstration in
real-world environments of one of the proposed memory benefits of these
neurons.Comment: Pre-print of article that will appear in the IEEE Robotics and
Automation Letter
Look No Further: Adapting the Localization Sensory Window to the Temporal Characteristics of the Environment
Many localization algorithms use a spatiotemporal window of sensory
information in order to recognize spatial locations, and the length of this
window is often a sensitive parameter that must be tuned to the specifics of
the application. This letter presents a general method for environment-driven
variation of the length of the spatiotemporal window based on searching for the
most significant localization hypothesis, to use as much context as is
appropriate but not more. We evaluate this approach on benchmark datasets using
visual and Wi-Fi sensor modalities and a variety of sensory comparison
front-ends under in-order and out-of-order traversals of the environment. Our
results show that the system greatly reduces the maximum distance traveled
without localization compared to a fixed-length approach while achieving
competitive localization accuracy, and our proposed method achieves this
performance without deployment-time tuning.Comment: Pre-print of article appearing in 2017 IEEE Robotics and Automation
Letters. v2: incorporated reviewer feedbac
Feature Map Filtering: Improving Visual Place Recognition with Convolutional Calibration
Convolutional Neural Networks (CNNs) have recently been shown to excel at
performing visual place recognition under changing appearance and viewpoint.
Previously, place recognition has been improved by intelligently selecting
relevant spatial keypoints within a convolutional layer and also by selecting
the optimal layer to use. Rather than extracting features out of a particular
layer, or a particular set of spatial keypoints within a layer, we propose the
extraction of features using a subset of the channel dimensionality within a
layer. Each feature map learns to encode a different set of weights that
activate for different visual features within the set of training images. We
propose a method of calibrating a CNN-based visual place recognition system,
which selects the subset of feature maps that best encodes the visual features
that are consistent between two different appearances of the same location.
Using just 50 calibration images, all collected at the beginning of the current
environment, we demonstrate a significant and consistent recognition
improvement across multiple layers for two different neural networks. We
evaluate our proposal on three datasets with different types of appearance
changes - afternoon to morning, winter to summer and night to day.
Additionally, the dimensionality reduction approach improves the computational
processing speed of the recognition system.Comment: Accepted to the Australasian Conference on Robotics and Automation
201
- …